hidimstat.permutation_test_cv

hidimstat.permutation_test_cv(X, y, n_permutations=1000, C=None, Cs=array([1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01]), seed=0, n_jobs=1, verbose=1)

Cross-validated permutation test shuffling the target

Parameters:

Xndarray, shape (n_samples, n_features)

Data.

yndarray, shape (n_samples,)

Target.

Cfloat or None, optional (default=None)

If None, the linear SVR regularization parameter is set by cross-val running a grid search on the list of hyper-parameters contained in Cs. Otherwise, the regularization parameter is equal to C. The strength of the regularization is inversely proportional to C.

Csndarray, optional (default=np.logspace(-7, 1, 9))

If C is None, the linear SVR regularization parameter is set by cross-val running a grid search on the list of hyper-parameters contained in Cs.

n_permutationsint, optional (default=1000)

Number of permutations used to compute the survival function and cumulative distribution function scores.

seedint, optional (default=0)

Determines the permutations used for shuffling the target

n_jobsint or None, optional (default=1)

Number of CPUs to use during the cross validation.

verbose: int, optional (default=1)

The verbosity level: if non zero, progress messages are printed when computing the permutation stats in parralel. The frequency of the messages increases with the verbosity level.

Returns:

pval_corrndarray, shape (n_features,)

p-value corrected for multiple testing, with numerically accurate values for positive effects (ie., for p-value close to zero).

one_minus_pval_corrndarray, shape (n_features,)

One minus the corrected p-value, with numerically accurate values for negative effects (ie., for p-value close to one).